----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 1 Estimates - Whites Only.log
  log type:  text
 opened on:  19 Jan 2016, 08:00:24

. do "C:\Users\cdj19\AppData\Local\Temp\STD01000000.tmp"

. ***** Data: Meritocracy Replication Data - Table 1 (for Table 1 - Whites Only) *****
. 
. ** Model reported in paper: Logit model w/ random intercept & random slope **
. 
. xtmelogit meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==1 || fips: income_i, cov(unstruct)

Refining starting values: 

Iteration 0:   log likelihood = -3225.1109  
Iteration 1:   log likelihood = -3149.7883  
Iteration 2:   log likelihood = -3130.0982  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -3130.0982  
Iteration 1:   log likelihood =  -3124.121  
Iteration 2:   log likelihood = -3123.1314  
Iteration 3:   log likelihood = -3123.0635  
Iteration 4:   log likelihood = -3123.0627  
Iteration 5:   log likelihood = -3123.0627  

Mixed-effects logistic regression               Number of obs      =      6438
Group variable: fips                            Number of groups   =      1688

                                                Obs per group: min =         1
                                                               avg =       3.8
                                                               max =        87

Integration points =   7                        Wald chi2(18)      =    460.42
Log likelihood = -3123.0627                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   1.695757   .8170026     2.08   0.038     .0944614    3.297053
         income_i |  -.0959963   .5359757    -0.18   0.858    -1.146489    .9544967
ginicntyXincome_i |  -2.646177   1.238335    -2.14   0.033    -5.073269   -.2190843
      income_cnty |  -.5166121   .2510304    -2.06   0.040    -1.008623   -.0246015
       black_cnty |  -.3809536   .2790223    -1.37   0.172    -.9278272      .16592
      perc_bush04 |  -.1625697   .3101227    -0.52   0.600    -.7703989    .4452596
         pop_cnty |   -.136354   .2678355    -0.51   0.611    -.6613019    .3885939
           educ_i |  -.6630842   .1358602    -4.88   0.000    -.9293652   -.3968031
            age_i |  -.0009408   .0020464    -0.46   0.646    -.0049518    .0030701
         gender_i |   .0205977   .0653498     0.32   0.753    -.1074856     .148681
          unemp_i |   .1129916     .08447     1.34   0.181    -.0525665    .2785497
          union_i |   .1608522   .0921525     1.75   0.081    -.0197633    .3414678
        partyid_i |   -.774924   .1023198    -7.57   0.000    -.9754672   -.5743809
           ideo_i |  -.4980468   .1581799    -3.15   0.002    -.8080736   -.1880199
         attend_i |  -.2337348   .1070119    -2.18   0.029    -.4434743   -.0239954
       survid2006 |   .0287269   .0936105     0.31   0.759    -.1547463    .2122002
       survid2007 |  -1.001659    .099727   -10.04   0.000     -1.19712   -.8061977
       survid2009 |  -.8847213   .0893438    -9.90   0.000    -1.059832   -.7096107
            _cons |   .7046684    .432786     1.63   0.103    -.1435765    1.552913
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Unstructured           |
                sd(income_i) |   .9212026    .490342      .3245441    2.614789
                   sd(_cons) |   .6124695   .3285644       .214018    1.752745
        corr(income_i,_cons) |         -1   .0000221            -1           1
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(3) =     1.08   Prob > chi2 = 0.7815

Note: LR test is conservative and provided only for reference.

. 
. ** Alternative specifications **
. 
. * Linear probability model w/clustered ses *
. 
. reg meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==1, cluster(fips)

Linear regression                                      Number of obs =    6438
                                                       F( 18,  1687) =   33.28
                                                       Prob > F      =  0.0000
                                                       R-squared     =  0.0816
                                                       Root MSE      =  .39763

                                     (Std. Err. adjusted for 1688 clusters in fips)
-----------------------------------------------------------------------------------
                  |               Robust
      meritocracy |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   .2855425   .1351601     2.11   0.035     .0204434    .5506417
         income_i |  -.0185048   .0804674    -0.23   0.818    -.1763313    .1393217
ginicntyXincome_i |  -.4107991   .1801785    -2.28   0.023     -.764196   -.0574023
      income_cnty |  -.0723269   .0352547    -2.05   0.040    -.1414745   -.0031794
       black_cnty |  -.0565361   .0415767    -1.36   0.174    -.1380835    .0250113
      perc_bush04 |  -.0222399   .0470501    -0.47   0.636    -.1145225    .0700427
         pop_cnty |  -.0231102    .022095    -1.05   0.296    -.0664468    .0202263
           educ_i |  -.1081085   .0214993    -5.03   0.000    -.1502766   -.0659403
            age_i |  -.0001351   .0003399    -0.40   0.691    -.0008018    .0005316
         gender_i |   .0041541    .009896     0.42   0.675    -.0152556    .0235638
          unemp_i |   .0159068   .0136256     1.17   0.243     -.010818    .0426316
          union_i |   .0232376    .014852     1.56   0.118    -.0058926    .0523678
        partyid_i |  -.1279631   .0179021    -7.15   0.000    -.1630758   -.0928503
           ideo_i |  -.0716865   .0270825    -2.65   0.008    -.1248053   -.0185676
         attend_i |  -.0356049   .0176389    -2.02   0.044    -.0702014   -.0010084
       survid2006 |   .0017416   .0178988     0.10   0.922    -.0333646    .0368478
       survid2007 |  -.1636673   .0160057   -10.23   0.000    -.1950604   -.1322742
       survid2009 |  -.1475493   .0150867    -9.78   0.000    -.1771398   -.1179587
            _cons |   .5475725   .0690606     7.93   0.000     .4121191    .6830259
-----------------------------------------------------------------------------------

. 
. * Logit model w/ clustered ses *
. 
. logit meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==1, cluster(fips)

Iteration 0:   log pseudolikelihood = -3393.0433  
Iteration 1:   log pseudolikelihood = -3132.9422  
Iteration 2:   log pseudolikelihood = -3123.6287  
Iteration 3:   log pseudolikelihood = -3123.6035  
Iteration 4:   log pseudolikelihood = -3123.6035  

Logistic regression                               Number of obs   =       6438
                                                  Wald chi2(18)   =     529.80
                                                  Prob > chi2     =     0.0000
Log pseudolikelihood = -3123.6035                 Pseudo R2       =     0.0794

                                     (Std. Err. adjusted for 1688 clusters in fips)
-----------------------------------------------------------------------------------
                  |               Robust
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   1.708533   .7866611     2.17   0.030     .1667058    3.250361
         income_i |  -.0874442   .5252069    -0.17   0.868    -1.116831    .9419424
ginicntyXincome_i |  -2.681546   1.188772    -2.26   0.024    -5.011497   -.3515957
      income_cnty |  -.5120772   .2421354    -2.11   0.034    -.9866539   -.0375005
       black_cnty |  -.3600649    .273196    -1.32   0.188    -.8955193    .1753894
      perc_bush04 |  -.1748706   .3009364    -0.58   0.561    -.7646952    .4149539
         pop_cnty |   -.128206   .1471621    -0.87   0.384    -.4166384    .1602265
           educ_i |  -.6558135   .1310667    -5.00   0.000    -.9126995   -.3989275
            age_i |  -.0010573   .0020539    -0.51   0.607     -.005083    .0029683
         gender_i |   .0217205   .0636203     0.34   0.733    -.1029729     .146414
          unemp_i |   .1140097   .0891019     1.28   0.201    -.0606267    .2886462
          union_i |   .1576013   .0889691     1.77   0.076    -.0167749    .3319775
        partyid_i |  -.7700466   .1054519    -7.30   0.000    -.9767286   -.5633646
           ideo_i |  -.4953939   .1593593    -3.11   0.002    -.8077325   -.1830553
         attend_i |  -.2306359   .1087414    -2.12   0.034    -.4437651   -.0175066
       survid2006 |   .0282102   .0925727     0.30   0.761     -.153229    .2096494
       survid2007 |  -.9935107   .0984425   -10.09   0.000    -1.186454   -.8005669
       survid2009 |  -.8797201   .0860237   -10.23   0.000    -1.048323   -.7111168
            _cons |   .7068101    .416733     1.70   0.090    -.1099716    1.523592
-----------------------------------------------------------------------------------

. 
. * Linear probability model w/ random intercept *
. 
. xtmixed meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==1 || fips: , 

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood =  -3216.695  
Iteration 1:   log likelihood = -3188.3526  
Iteration 2:   log likelihood = -3188.3252  
Iteration 3:   log likelihood = -3188.3251  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      6438
Group variable: fips                            Number of groups   =      1688

                                                Obs per group: min =         1
                                                               avg =       3.8
                                                               max =        87


                                                Wald chi2(18)      =    572.15
Log likelihood = -3188.3251                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   .2855425   .1274969     2.24   0.025     .0356533    .5354318
         income_i |  -.0185048   .0792931    -0.23   0.815    -.1739165    .1369069
ginicntyXincome_i |  -.4107991   .1803504    -2.28   0.023    -.7642793   -.0573189
      income_cnty |  -.0723269   .0376134    -1.92   0.054    -.1460478    .0013939
       black_cnty |  -.0565361   .0424622    -1.33   0.183    -.1397604    .0266883
      perc_bush04 |  -.0222399     .04789    -0.46   0.642    -.1161026    .0716227
         pop_cnty |  -.0231102   .0402719    -0.57   0.566    -.1020417    .0558213
           educ_i |  -.1081085   .0215068    -5.03   0.000     -.150261    -.065956
            age_i |  -.0001351   .0003265    -0.41   0.679     -.000775    .0005048
         gender_i |   .0041541   .0101825     0.41   0.683    -.0158032    .0241114
          unemp_i |   .0159068   .0129548     1.23   0.219    -.0094842    .0412978
          union_i |   .0232376   .0148457     1.57   0.118    -.0058595    .0523347
        partyid_i |  -.1279631   .0164967    -7.76   0.000     -.160296   -.0956302
           ideo_i |  -.0716865   .0255592    -2.80   0.005    -.1217815   -.0215914
         attend_i |  -.0356049   .0169369    -2.10   0.036    -.0688006   -.0024091
       survid2006 |   .0017416   .0161847     0.11   0.914    -.0299797     .033463
       survid2007 |  -.1636673   .0155299   -10.54   0.000    -.1941054   -.1332292
       survid2009 |  -.1475493   .0144114   -10.24   0.000    -.1757951   -.1193034
            _cons |   .5475725   .0673202     8.13   0.000     .4156273    .6795177
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   4.80e-07   .0002765             0           .
-----------------------------+------------------------------------------------
                sd(Residual) |    .397046   .0034991      .3902467    .4039637
------------------------------------------------------------------------------
LR test vs. linear regression: chibar2(01) =     0.00 Prob >= chibar2 = 1.0000

. 
. * Logit model w/ random intercept *
. 
. xtmelogit meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==1 || fips: , 

Refining starting values: 

Iteration 0:   log likelihood = -3195.6885  
Iteration 1:   log likelihood = -3163.7396  
Iteration 2:   log likelihood = -3131.8704  

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -3131.8704  
Iteration 1:   log likelihood = -3124.0589  
Iteration 2:   log likelihood = -3123.6398  
Iteration 3:   log likelihood = -3123.6038  
Iteration 4:   log likelihood = -3123.6035  
Iteration 5:   log likelihood = -3123.6035  

Mixed-effects logistic regression               Number of obs      =      6438
Group variable: fips                            Number of groups   =      1688

                                                Obs per group: min =         1
                                                               avg =       3.8
                                                               max =        87

Integration points =   7                        Wald chi2(18)      =    480.30
Log likelihood = -3123.6035                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   1.708532   .7823651     2.18   0.029      .175125     3.24194
         income_i |   -.087445   .5159115    -0.17   0.865    -1.098613     .923723
ginicntyXincome_i |  -2.681545   1.183891    -2.27   0.024    -5.001928   -.3611609
      income_cnty |  -.5120776      .2486    -2.06   0.039    -.9993245   -.0248306
       black_cnty |   -.360065   .2750672    -1.31   0.191    -.8991868    .1790567
      perc_bush04 |  -.1748707   .3059824    -0.57   0.568    -.7745852    .4248438
         pop_cnty |  -.1282064   .2656275    -0.48   0.629    -.6488266    .3924139
           educ_i |  -.6558133   .1344171    -4.88   0.000    -.9192659   -.3923607
            age_i |  -.0010573   .0020198    -0.52   0.601    -.0050162    .0029015
         gender_i |   .0217206   .0648786     0.33   0.738    -.1054391    .1488802
          unemp_i |   .1140097   .0837525     1.36   0.173    -.0501422    .2781616
          union_i |   .1576013   .0915976     1.72   0.085    -.0219268    .3371293
        partyid_i |  -.7700467   .1013333    -7.60   0.000    -.9686562   -.5714371
           ideo_i |  -.4953939   .1566599    -3.16   0.002    -.8024417   -.1883461
         attend_i |  -.2306359     .10605    -2.17   0.030    -.4384902   -.0227817
       survid2006 |   .0282101   .0928913     0.30   0.761    -.1538534    .2102737
       survid2007 |  -.9935108   .0984663   -10.09   0.000    -1.186501   -.8005204
       survid2009 |  -.8797202   .0882876    -9.96   0.000    -1.052761   -.7066797
            _cons |   .7068109   .4200232     1.68   0.092    -.1164194    1.530041
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Identity               |
                   sd(_cons) |   1.01e-09   .2158191             0           .
------------------------------------------------------------------------------
LR test vs. logistic regression: chibar2(01) =     0.00 Prob>=chibar2 = 1.0000

. 
. * Linear probability model w/ random intercept & random slope *
. 
. xtmixed meritocracy ginicnty income_i ginicntyXincome_i ///
>         income_cnty black_cnty perc_bush04 pop_cnty educ_i age_i gender_i unemp_i union_i partyid_i ideo_i attend_i ///
>         survid2006 survid2007 survid2009 if white==1 || fips: income_i, cov(unstruct)

Performing EM optimization: 

Performing gradient-based optimization: 

Iteration 0:   log likelihood = -3222.4937  
Iteration 1:   log likelihood = -3178.2121  
Iteration 2:   log likelihood = -3177.5296  
Iteration 3:   log likelihood = -3177.5279  
Iteration 4:   log likelihood = -3177.5275  
Iteration 5:   log likelihood = -3177.5274  
Iteration 6:   log likelihood = -3177.5274  
Iteration 7:   log likelihood = -3177.5274  

Computing standard errors:

Mixed-effects ML regression                     Number of obs      =      6438
Group variable: fips                            Number of groups   =      1688

                                                Obs per group: min =         1
                                                               avg =       3.8
                                                               max =        87


                                                Wald chi2(18)      =    549.64
Log likelihood = -3177.5274                     Prob > chi2        =    0.0000

-----------------------------------------------------------------------------------
      meritocracy |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------+----------------------------------------------------------------
         ginicnty |   .2615963    .142525     1.84   0.066    -.0177476    .5409403
         income_i |  -.0361029   .0849968    -0.42   0.671    -.2026936    .1304878
ginicntyXincome_i |  -.3668082    .198221    -1.85   0.064    -.7553142    .0216978
      income_cnty |  -.0694177   .0383328    -1.81   0.070    -.1445486    .0057131
       black_cnty |  -.0613957   .0436254    -1.41   0.159    -.1468999    .0241085
      perc_bush04 |  -.0056538   .0493643    -0.11   0.909     -.102406    .0910984
         pop_cnty |  -.0241216   .0434817    -0.55   0.579    -.1093442    .0611011
           educ_i |  -.1101767   .0214933    -5.13   0.000    -.1523028   -.0680506
            age_i |  -.0000364   .0003276    -0.11   0.912    -.0006784    .0006056
         gender_i |   .0038636   .0101358     0.38   0.703    -.0160022    .0237294
          unemp_i |    .015403   .0129269     1.19   0.233    -.0099332    .0407393
          union_i |    .024757   .0147102     1.68   0.092    -.0040744    .0535885
        partyid_i |  -.1264698   .0164584    -7.68   0.000    -.1587278   -.0942118
           ideo_i |  -.0736843   .0255154    -2.89   0.004    -.1236936   -.0236749
         attend_i |  -.0365215   .0169089    -2.16   0.031    -.0696624   -.0033807
       survid2006 |   .0020752   .0161132     0.13   0.898    -.0295061    .0336565
       survid2007 |  -.1629094   .0154838   -10.52   0.000    -.1932572   -.1325616
       survid2009 |  -.1472971   .0143862   -10.24   0.000    -.1754935   -.1191006
            _cons |   .5447131   .0719252     7.57   0.000     .4037423    .6856838
-----------------------------------------------------------------------------------

------------------------------------------------------------------------------
  Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
fips: Unstructured           |
                sd(income_i) |   .2705556   .0436665      .1971861    .3712247
                   sd(_cons) |   .2198901   .0304828      .1675737    .2885396
        corr(income_i,_cons) |         -1   7.05e-06            -1           1
-----------------------------+------------------------------------------------
                sd(Residual) |    .389935   .0037642      .3826266     .397383
------------------------------------------------------------------------------
LR test vs. linear regression:       chi2(3) =    21.60   Prob > chi2 = 0.0001

Note: LR test is conservative and provided only for reference.

. 
end of do-file

. log close
      name:  <unnamed>
       log:  C:\Users\cdj19\Desktop\Replication AJPS\Table 1 Estimates - Whites Only.log
  log type:  text
 closed on:  19 Jan 2016, 08:20:58
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